To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("rfPred")
In most cases, you don't need to download the package archive at all.
rfPred
Assign rfPred functional prediction scores to a missense variants list
Bioconductor version: 3.0
Based on external numerous data files where rfPred scores are pre-calculated on all genomic positions of the human exome, the package gives rfPred scores to missense variants identified by the chromosome, the position (hg19 version), the referent and alternative nucleotids and the uniprot identifier of the protein. Note that for using the package, the user has to be connected on the Internet or to download the TabixFile and index (approximately 3.3 Go).
Author: Fabienne Jabot-Hanin, Hugo Varet and Jean-Philippe Jais
Maintainer: Hugo Varet <varethugo at gmail.com>
Citation (from within R,
enter citation("rfPred")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("rfPred")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("rfPred")
| R Script | CalculatingrfPredscoreswithpackagerfPred | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Annotation, Classification, Software |
| Version | 1.4.0 |
| In Bioconductor since | BioC 2.13 (R-3.0) |
| License | GPL (>=2 ) |
| Depends | Rsamtools, GenomicRanges, IRanges, data.table, methods, parallel |
| Imports | |
| LinkingTo | |
| Suggests | BiocStyle |
| SystemRequirements | |
| Enhances | |
| URL | http://www.sbim.fr/rfPred |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | rfPred_1.4.0.tar.gz |
| Windows Binary | rfPred_1.4.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | rfPred_1.4.0.tgz |
| Mac OS X 10.9 (Mavericks) | rfPred_1.4.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
- RNA-Seq workflow: gene-level exploratory analysis and differential expression
- Changing genomic coordinate systems with rtracklayer::liftOver
- Mass spectrometry and proteomics data analysis
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